Marine microorganisms play an irreplaceable role in removing spilled oil. Zhoushan archipelago has one of the busiest ports and oil stockpiles in China. However, little is known about which and how fast oil-degrading 14 3 microorganisms could biodegrade spilled oil here. By combining C-/ H-based radiotracer assays and MiSeq sequencing, we report the successive pattern of microbial oil-degrading activities and community compositions. The biodegradation rates of alkanes and PAHs (Polycyclic Aromatic Hydrocarbons) were significantly stimulated by oil addition, and reached their maximum after incubation for 3 and 7 days, respectively. alkB phnA Meanwhile, the abundances of and c genes increased and the bacterial communities continuously Alcanivorax Erythrobacter shifted. Potential oil-degrading bacteria , were the dominant degraders by day 3, C1-B045 Alteromonas Pseudohongiella whereas the dominant degraders shifted to , , in the later period. These results provide valuable insights into the cooperative system of the versatile oil-degrading bacteria in successively biodegrading complex oil hydrocarbons in oil spills.

Pereira, E., Meirelles, A.J.A., Maximo, G.J., 2020. Predictive models for physical properties of fats, oils, and biodiesel fuels. Fluid Phase Equilibria 508, 112440.

http://www.sciencedirect.com/science/article/pii/S0378381219305023 Physicochemical properties determine in large extent the application of fats and oils, considering products' formulation, and the design of equipment and processes in the food industry. These lipids have also been highly significant in the development of the bioenergy industry due to the increasing demand for biodiesel worldwide, whose physical properties should attend strict standards specifications. Fats, oils, and biodiesel are multicomponent systems which present complex phase equilibrium profile. Thus, the phase equilibrium behavior of these lipids plays a crucial role in their physicochemical properties. In this context, several thermodynamic modeling approaches have been used to predict the behavior of lipids and design products with desired properties. In this paper, we critically review and summarize the current state of knowledge of predictive modeling approaches used for the calculation of physicochemical properties of fats, oils, and biodiesel with a special focus on properties related to product design, such as melting/crystallization behavior and viscosity. The most remarkable publications dealing with predictive modeling are analyzed, and the underlying thermodynamic concepts used in those approaches, as well as their limitations, are assessed. Finally, we discuss future perspectives and indicate challenges to improve the research area.

Pérez-Guaita, D., Quintás, G., Kuligowski, J., 2020. Discriminant analysis and feature selection in mass spectrometry imaging using constrained repeated random sampling - Cross validation (CORRS-CV). Analytica Chimica Acta 1097, 30-36.

http://www.sciencedirect.com/science/article/pii/S0003267019312668 The identification of biomarkers through Mass spectrometry imaging (MSI) is gaining popularity in the clinical field. However, considering the complexity of spectral and spatial variables faced, data mining of the hyperspectral images can be troublesome. The discovery of markers generally depends on the creation of m z classification models which should be validated to ensure the statistical significance of the discriminants / detected. Internal validation using resampling methods such as cross validation (CV) are widely used for model selection, the estimation of its generalization performance and biomarker discovery when sample sizes are limited and an independent test set is not available. Here, we introduce for first time the use of Constrained Repeated Random Subsampling CV (CORRS-CV) on multi-images for the validation of classification models on MSI. Although several aspects must be taken into account (e.g. image size, CORRS- CV∂value, the similarity across spatially close pixels, the total computation time), CORRS-CV provides more accurate estimates of the k model performance than -fold CV using of biological replicates to define the data split when the number of biological replicates is scarce and holding images back for testing is a waste of valuable information. Besides, the combined use of CORRS-CV and rank products increases the robustness of the selection of discriminant features as candidate biomarkers which is an important issue due to the increased biological, environmental and technical variabilities when analysing multiple images, especially from human tissues collected in clinical studies.